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Found 9 papers, 7 papers with code
The TALP-UPC Participation in WMT21 News Translation Task: an mBART-based NMT Approach
We fine-tune mBART50 using the filtered data, and additionally, we train a Transformer model on the same data from scratch.
Pretrained Speech Encoders and Efficient Fine-tuning Methods for Speech Translation: UPC at IWSLT 2022
We further investigate the suitability of different speech encoders (wav2vec 2. 0, HuBERT) for our models and the impact of knowledge distillation from the Machine Translation model that we use for the decoder (mBART).
Pushing the Limits of Zero-shot End-to-End Speech Translation
The speech encoder seamlessly integrates with the MT model at inference, enabling direct translation from speech to text, across all languages supported by the MT model.
Speech Translation with Foundation Models and Optimal Transport: UPC at IWSLT23
Our Speech Translation systems utilize foundation models for speech (wav2vec 2. 0) and text (mBART50).
Explaining How Transformers Use Context to Build Predictions
Language Generation Models produce words based on the previous context.
SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations
End-to-end Speech Translation is hindered by a lack of available data resources.
Efficient Speech Translation with Dynamic Latent Perceivers
Transformers have been the dominant architecture for Speech Translation in recent years, achieving significant improvements in translation quality.
SHAS: Approaching optimal Segmentation for End-to-End Speech Translation
Speech translation datasets provide manual segmentations of the audios, which are not available in real-world scenarios, and existing segmentation methods usually significantly reduce translation quality at inference time.
End-to-End Speech Translation with Pre-trained Models and Adapters: UPC at IWSLT 2021
Our submission also uses a custom segmentation algorithm that employs pre-trained Wav2Vec 2. 0 for identifying periods of untranscribable text and can bring improvements of 2. 5 to 3 BLEU score on the IWSLT 2019 test set, as compared to the result with the given segmentation.
Ranked #2 on
Speech-to-Text Translation
on MuST-C EN->DE
(using extra training data)
